






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
      from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


SCREAMING_SNAKE_CASE__		:							Union[str, Any] 				=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:							List[Any] 				=				{
    """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
    ),
    """microsoft/deberta-v2-xxlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
    ),
}







class 				UpperCAmelCase_  (		__lowerCamelCase		):
     __lowerCamelCase      				=  'deberta-v2'




     def __init__(				self	,						_lowerCAmelCase=128100	,						_lowerCAmelCase=1536	,						_lowerCAmelCase=24	,						_lowerCAmelCase=24	,						_lowerCAmelCase=6144	,						_lowerCAmelCase="gelu"	,						_lowerCAmelCase=0.1	,						_lowerCAmelCase=0.1	,						_lowerCAmelCase=512	,						_lowerCAmelCase=0	,						_lowerCAmelCase=0.0_2	,						_lowerCAmelCase=1e-7	,						_lowerCAmelCase=False	,						_lowerCAmelCase=-1	,						_lowerCAmelCase=0	,						_lowerCAmelCase=True	,						_lowerCAmelCase=None	,						_lowerCAmelCase=0	,						_lowerCAmelCase="gelu"	,						**_lowerCAmelCase	,						):
          super().__init__(**_lowerCAmelCase		)

          UpperCAmelCase__      :  List[str]				      =			hidden_size
          UpperCAmelCase__      :  List[str]				      =			num_hidden_layers
          UpperCAmelCase__      :  Any				      =			num_attention_heads
          UpperCAmelCase__      :  List[str]				      =			intermediate_size
          UpperCAmelCase__      :  Optional[Any]				      =			hidden_act
          UpperCAmelCase__      :  Any				      =			hidden_dropout_prob
          UpperCAmelCase__      :  Tuple				      =			attention_probs_dropout_prob
          UpperCAmelCase__      :  Optional[Any]				      =			max_position_embeddings
          UpperCAmelCase__      :  Tuple				      =			type_vocab_size
          UpperCAmelCase__      :  Any				      =			initializer_range
          UpperCAmelCase__      :  List[Any]				      =			relative_attention
          UpperCAmelCase__      :  Tuple				      =			max_relative_positions
          UpperCAmelCase__      :  List[str]				      =			pad_token_id
          UpperCAmelCase__      :  Any				      =			position_biased_input

          # Backwards compatibility
          if type(_lowerCAmelCase		) == str:
               UpperCAmelCase__      :  Tuple				      =			[x.strip() for x in pos_att_type.lower().split("""|"""		)]

          UpperCAmelCase__      :  Dict				      =			pos_att_type
          UpperCAmelCase__      :  Tuple				      =			vocab_size
          UpperCAmelCase__      :  Dict				      =			layer_norm_eps

          UpperCAmelCase__      :  Any				      =			kwargs.get("""pooler_hidden_size"""	,						_lowerCAmelCase		)
          UpperCAmelCase__      :  Optional[int]				      =			pooler_dropout
          UpperCAmelCase__      :  int				      =			pooler_hidden_act







class 				UpperCAmelCase_  (		__lowerCamelCase		):




     @property
     def   __UpperCAmelCase  (				self		):
          if self.task == "multiple-choice":
               UpperCAmelCase__      :  Union[str, Any]				      =			{0: """batch""", 1: """choice""", 2: """sequence"""}
          else:
               UpperCAmelCase__      :  Tuple				      =			{0: """batch""", 1: """sequence"""}
          if self._config.type_vocab_size > 0:
               return OrderedDict(
                   [("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)]		)
          else:
               return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)]		)




     @property
     def   __UpperCAmelCase  (				self		):
          return 12




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase = -1	,						_lowerCAmelCase = -1	,						_lowerCAmelCase = -1	,						_lowerCAmelCase = False	,						_lowerCAmelCase = None	,						_lowerCAmelCase = 3	,						_lowerCAmelCase = 40	,						_lowerCAmelCase = 40	,						_lowerCAmelCase = None	,						):
          UpperCAmelCase__      :  int				      =			super().generate_dummy_inputs(preprocessor=_lowerCAmelCase	,						framework=_lowerCAmelCase		)
          if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
               del dummy_inputs["token_type_ids"]
          return dummy_inputs







import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class   _UpperCamelCase (		_UpperCAmelCase		,_UpperCAmelCase					):


			"""simple docstring"""






			@register_to_config
			def __init__(  self ,  *,
			    lowerCAmelCase__ = 4 ,  lowerCAmelCase__ = 7_68 ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  )	->   Dict:

							'''simple docstring'''


							super().__init__()

							__lowercase					     =				nn.Parameter(torch.zeros(lowerCAmelCase__		)		)

							# parameters for additional clip time embeddings
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)

							# parameters for encoder hidden states
							__lowercase					     =				clip_extra_context_tokens
							__lowercase					     =				nn.Linear(
							    lowerCAmelCase__ ,  self.clip_extra_context_tokens * cross_attention_dim		)
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				nn.LayerNorm(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  *, lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   List[str]:

							'''simple docstring'''


							if do_classifier_free_guidance:
											# Add the classifier free guidance embeddings to the image embeddings
											__lowercase					     =				image_embeddings.shape[0]
											__lowercase					     =				self.learned_classifier_free_guidance_embeddings.unsqueeze(0		)
											__lowercase					     =				classifier_free_guidance_embeddings.expand(
											    lowerCAmelCase__ ,  -1		)
											__lowercase					     =				torch.cat([classifier_free_guidance_embeddings, image_embeddings] ,  dim=0		)

							# The image embeddings batch size and the text embeddings batch size are equal
							assert image_embeddings.shape[0] == prompt_embeds.shape[0]

							__lowercase					     =				prompt_embeds.shape[0]

							# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
							# adding CLIP embeddings to the existing timestep embedding, ...
							__lowercase					     =				self.embedding_proj(lowerCAmelCase__		)
							__lowercase					     =				self.clip_image_embeddings_project_to_time_embeddings(lowerCAmelCase__		)
							__lowercase					     =				time_projected_image_embeddings + time_projected_prompt_embeds

							# ... and by projecting CLIP embeddings into four
							# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
							__lowercase					     =				self.clip_extra_context_tokens_proj(lowerCAmelCase__		)
							__lowercase					     =				clip_extra_context_tokens.reshape(lowerCAmelCase__ ,  -1 ,  self.clip_extra_context_tokens		)
							__lowercase					     =				clip_extra_context_tokens.permute(0 ,  2 ,  1		)

							__lowercase					     =				self.encoder_hidden_states_proj(lowerCAmelCase__		)
							__lowercase					     =				self.text_encoder_hidden_states_norm(lowerCAmelCase__		)
							__lowercase					     =				torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] ,  dim=1		)

							return text_encoder_hidden_states, additive_clip_time_embeddings